Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…Then, using a Machine Learning Algorithm, a model that allows the optimization of printing parameters and enables process control through an open-loop system was developed. …”
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Error Analysis and Adaptive-Robust Control of a 6-DoF Parallel Robot with Ball-Screw Drive Actuators by Navid Negahbani, Hermes Giberti, Enrico Fiore

“…Finally, a nonlinear adaptive-robust control algorithm for trajectory tracking, based on the minimization of the tracking error, is described and simulated.…”
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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays by Sina Hasibi Taheri, Javad Mohammadpour, Ali Lalbakhsh, Slawomir Koziel, Stanislaw Szczepanski

“…To broaden the method’s applicability and meet manufacturing requirements, it also considers dielectric materials other than air between the array and WAIM. Machine learning (ML) algorithms are integrated to evaluate WAIM characteristics, reducing calculation time and resources while enhancing adaptability to new structures with minimal designer intervention. …”
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Two Decades of Coastal Development across the Arabian Gulf: Insights from Remote Sensing and Machine Learning by B. Dahy, R. Sousa, M. Sultan, J. A. Burt

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A deep learning model with machine vision system for recognizing type of the food during the food consumption by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…The primary objective was to utilize machine vision and deep learning to identify consumed food products. …”
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Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. …”
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Local adaptation and validation of a transdiagnostic risk calculator for first episode psychosis using mental health patient records by Elizabeth Ford, James Stone, James Stone, James Stone, Dominic Oliver, Dominic Oliver, Dominic Oliver, Benjamin Fell, Gloria Roque, Sam Robertson, Paolo Fusar-Poli, Paolo Fusar-Poli, Paolo Fusar-Poli, Paolo Fusar-Poli, Kathryn Greenwood, Kathryn Greenwood

“…We developed new machine-learning NLP algorithms for diagnosis, symptom and substance use concepts by fine-tuning existing open-source transformer models. …”
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Using machine learning techniques to evaluate the impact of future climate change on wheat yields in Xinjiang, China by Xuehui Gao, Jian Liu, Haixia Lin, Tehseen Javed, Feihu Yin, Rui Chen, Yue Wen, Jinzhu Zhang, Kefan Yi, Zhenhua Wang

“…Additionally, the impacts of climate change scenarios on wheat yield were predicted using two emission scenarios (SSP45 and SSP85) from global climate models (GCMs) and machine learning (ML) algorithms. Results showed that climate variability is more prominent during the winter wheat growing season, yet yield variability is higher for spring wheat, with coefficients of variation ranging from 0.06–0.25 for spring wheat and 0.02–0.09 for winter wheat. …”
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle–Light Detection and Ranging and Machine Learning by Yan Yan, Jingjing Lei, Yuqing Huang

“…In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.…”
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Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

“…These findings provide valuable guidance for companies on choosing effective anti-fraud strategies and shed light on the adaptability of algorithms to real financial contexts, contributing to machine learning-based fraud detection.…”
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Ensemble Techniques for Robust Fake News Detection: Integrating Transformers, Natural Language Processing, and Machine Learning by Mohammed Al-alshaqi, Danda B. Rawat, Chunmei Liu

“…For textual data, the Random Forest classifier achieved 99% accuracy, outperforming other algorithms. The multimodal approach showed superior performance compared to baseline models, with a 3.1% accuracy improvement over existing multimodal techniques. …”
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Breeding of Solanaceous Crops Using AI: Machine Learning and Deep Learning Approaches—A Critical Review by Maria Gerakari, Anastasios Katsileros, Konstantina Kleftogianni, Eleni Tani, Penelope J. Bebeli, Vasileios Papasotiropoulos

“…This review discusses the potential of artificial intelligence (AI), particularly machine learning (ML) and its subset, deep learning (DL), in advancing the genetic improvement of Solanaceous crops. …”
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Predicting Alzheimer's Disease onset: A machine learning framework for early diagnosis using biomarker data by Shehu Mohammed, Neha Malhotra

“…In response to the formulated research problem, this study articulates a new multimodal machine-learning framework for early AD diagnosis. …”
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Modeling Air Combat Behavior for Simulation-Based Pilot Training: A Survey of Machine Learning Approaches by Andreas Strand, Patrick Ribu Gorton, Karsten Brathen

“…Recent advancements in machine learning, specifically reinforcement learning, imitation learning, and evolutionary algorithms, offer scalable alternatives by enabling agents to learn complex behavior from data. …”
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Lymph node metastasis in patients with hepatocellular carcinoma using machine learning: a population-based study by Li Yuqin, Li Yuqin, Li Hongyan, Li Hongyan, Li Hongyuan, Li Tingting, He Kun, Fang Jie, Han Yunhui

“…Seven LNM risk indicators were selected. Four machine learning algorithms—decision tree (DT), logistic Regression (LR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)—were employed to construct prediction models. …”
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Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models. by Al Mahmud Siam, Pankaj Bhowmik, Md Palash Uddin

“…To validate the proposed approach, we test it on five diverse datasets with varying characteristics and imbalance levels, employing five state-of-the-art machine learning algorithms: Random Forest (RF), Extra Trees (ET), XGBoost (XGBC), AdaBoost, and CatBoost. …”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. …”
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Extremist Ideology Classification in Kazakh: A Multi-Class Approach Using Machine Learning and Psycholinguistic Analysis by Shynar Mussiraliyeva, Milana Bolatbek, Kymbat Baisylbayeva

“…We employ a hybrid methodology that combines traditional text vectorization techniques, machine learning algorithms, and a psycholinguistic analysis module (PLAM) specifically adapted for the Kazakh language. …”
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Revolutionizing Nursing and Midwifery Informatics Curriculum Evaluation in Ghana: A Data-Driven Machine Learning Approach by Iven Aabaah, Japheth Kodua Wiredu, Bakaweri Emmanuel Batowise, Nelson Abuba Seidu

“…The study employed Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression algorithms, evaluated using standard performance metrics, including accuracy, precision, and recall. …”
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Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades, Alfonso J. López Rivero

“…This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. …”
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